PHILADELPHIA, Pa.—Researchers have discovered a
sophisticated computational algorithm that, when applied to a large set of gene
markers, has achieved greater accuracy than conventional methods in assessing
individual risk for type 1 diabetes.

Genome-wide association studies (GWAS), in which automated
genotyping tools scan the entire human genome seeking gene variants that
contribute to disease risk, have yet to fulfill their potential in allowing
physicians to accurately predict a person's individual risk for a disease, and
thus guide prevention and treatment strategies.

"For type 1 diabetes (TID), it means that we could identify
a high-risk group of individuals who would be followed and monitored closely
for earliest signs of T1D (before any islet cell destruction) and intervention
would begin with anti-T cell drugs—or other immunosuppressive drugs—to prevent
T1D from developing," Hakonarson says. "We believe also that development of a
new drugs that block the CLEC16A (formerly KIAA0350) signaling pathway in NK
cells will become a specific future preventive therapy for T1D."

Hakonarson points out that this approach can also be applied
in other disease areas, with inflammation and autoimmunity most effective.

"We are currently applying this algorithm on other GWAS data
we have and we see marked improvement in disease prediction of other
inflammatory/autoimmune disorders," he says. "This method is likely to work
well on diseases that are highly heritable."

According to Hakonarson, for many diseases, the majority of
contributory genes remain undiscovered, and studies that make selective use of
a limited number of selected, validated gene variants yield very limited
results.

"For many of the recent studies, the area under the curve
(AUC), a method of measuring the accuracy of risk assessment, amounts to 0.55
to 0.60, little better than chance (0.50), and thus falling short of clinical
usefulness," he says.

Hakonarson's team broadened its net, going beyond
cherry-picked susceptibility genes to searching a broader collection of
markers, including many that have not yet been confirmed, but which reach a
statistical threshold for gene interactions or association with a disease.
Although this approach embraces some false positives, its overall statistical
power produces robust predictive results.

By applying a "machine-learning" algorithm that finds
interactions among data points, say the authors, they were able to identify a
large ensemble of genes that interact together. After applying their algorithm
to a GWAS dataset for type 1 diabetes, they generated a model and then
validated that model in two independent datasets. The model was highly accurate
in separating type 1 diabetes cases from control subjects, achieving AUC scores
in the mid-80s.

Hakonarson points out that it is crucial to choose a target
disease carefully.

"Type 1 diabetes is known to be highly heritable, with many
risk-conferring genes concentrated in one region—the major histocompatibility
complex," he notes.

For other complex diseases, such as psychiatric disorders,
which do not have major-effect genes in concentrated locations, this approach
might not be as effective.

Furthermore, the researchers' risk assessment model might
not be applicable to mass population-level screening, but rather could be most
useful in evaluating siblings of affected patients, who already are known to
have a higher risk for the specific disease.

Hakonarson says the team's approach is more effective, and
costs less, than human leukocyte antigen (HLA) testing, currently used to
assess type 1 diabetes risk in clinical settings.

"We would like to see this test reach the market so we can
inform subjects at high risk in a better way and give them more options," notes
Harkonarson. "We will measure the impact we will have on clinical care in the
future."

The researchers used data provided by the Wellcome Trust
Case Control Consortium and the Genetics of Kidneys in Diabetes study.
Hakonarson's co-authors from The Children's Hospital of Philadelphia were Kai
Wang, Struan Grant, Haitao Zhang, Jonathan Bradfield, Cecilia Kim, Edward
Frackleton, Cuiping Hou, Joseph T. Glessner and Rosetta Chiavacci, all of the
Center for Applied Genomics; Dr. Charles Stanley of the Division of
Endocrinology; and Dr. Dimitri Monos of the Department of Pathology and
Laboratory Medicine. Other co-authors were Constantin Polychronakos and Hui Qi
Qu of McGill University in Montreal; and Zhi Wei of the New Jersey Institute of
Technology.